Python 3 and Feature Engineering / / Oswald Campesato.
This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python...
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Superior document: | Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2023 English |
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Place / Publishing House: | Dulles, VA : : Mercury Learning and Information, , [2023] ©2023 |
Year of Publication: | 2023 |
Language: | English |
Online Access: | |
Physical Description: | 1 online resource (216 p.) |
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100 | 1 | |a Campesato, Oswald, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
245 | 1 | 0 | |a Python 3 and Feature Engineering / |c Oswald Campesato. |
264 | 1 | |a Dulles, VA : |b Mercury Learning and Information, |c [2023] | |
264 | 4 | |c ©2023 | |
300 | |a 1 online resource (216 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
347 | |a text file |b PDF |2 rda | ||
505 | 0 | 0 | |t Frontmatter -- |t Contents -- |t Preface -- |t Chapter 1: Working With Datasets -- |t Chapter 2: Outlier and Anomaly Detection -- |t Chapter 3: Data Cleaning Tasks -- |t Chapter 4: Data Wrangling -- |t Chapter 5: Feature Selection -- |t Chapter 6: Feature Engineering -- |t Chapter 7: Dimensionality Reduction -- |t Appendix: Working With awk -- |t Index |
506 | 0 | |a restricted access |u http://purl.org/coar/access_right/c_16ec |f online access with authorization |2 star | |
520 | |a This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you’ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you’ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework. | ||
530 | |a Issued also in print. | ||
538 | |a Mode of access: Internet via World Wide Web. | ||
546 | |a In English. | ||
588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 02. Jun 2024) | |
650 | 0 | |a Data mining. | |
650 | 0 | |a Data sets. | |
650 | 0 | |a Machine learning. | |
650 | 0 | |a Python (Computer program language). | |
650 | 7 | |a COMPUTERS / Desktop Applications / Spreadsheets. |2 bisacsh | |
653 | |a data science, machine learning, Python, datasets, data wrangling, awk, artificial intelligence. | ||
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